A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.
BMC Med Inform Decis Mak
; 24(1): 90, 2024 Mar 28.
Article
em En
| MEDLINE
| ID: mdl-38549123
ABSTRACT
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
Idioma:
En
Revista:
BMC Med Inform Decis Mak
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Reino Unido